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"""
This is an example DAG which uses the DatabricksSubmitRunOperator.
In this example, we create two tasks which execute sequentially.
The first task is to run a notebook at the workspace path "/test"
and the second task is to run a JAR uploaded to DBFS. Both,
tasks use new clusters.

Because we have set a downstream dependency on the notebook task,
the spark jar task will NOT run until the notebook task completes
successfully.

The definition of a successful run is if the run has a result_state of "SUCCESS".
For more information about the state of a run refer to
https://docs.databricks.com/api/latest/jobs.html#runstate
"""
from __future__ import annotations

import os
from datetime import datetime

from airflow import DAG
from airflow.providers.databricks.operators.databricks import DatabricksSubmitRunOperator

ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID")
DAG_ID = "example_databricks_operator"

with DAG(
    dag_id=DAG_ID,
    schedule="@daily",
    start_date=datetime(2021, 1, 1),
    tags=["example"],
    catchup=False,
) as dag:
    # [START howto_operator_databricks_json]
    # Example of using the JSON parameter to initialize the operator.
    new_cluster = {
        "spark_version": "9.1.x-scala2.12",
        "node_type_id": "r3.xlarge",
        "aws_attributes": {"availability": "ON_DEMAND"},
        "num_workers": 8,
    }

    notebook_task_params = {
        "new_cluster": new_cluster,
        "notebook_task": {
            "notebook_path": "/Users/airflow@example.com/PrepareData",
        },
    }

    notebook_task = DatabricksSubmitRunOperator(task_id="notebook_task", json=notebook_task_params)
    # [END howto_operator_databricks_json]

    # [START howto_operator_databricks_named]
    # Example of using the named parameters of DatabricksSubmitRunOperator
    # to initialize the operator.
    spark_jar_task = DatabricksSubmitRunOperator(
        task_id="spark_jar_task",
        new_cluster=new_cluster,
        spark_jar_task={"main_class_name": "com.example.ProcessData"},
        libraries=[{"jar": "dbfs:/lib/etl-0.1.jar"}],
    )
    # [END howto_operator_databricks_named]
    notebook_task >> spark_jar_task

    from tests.system.utils.watcher import watcher

    # This test needs watcher in order to properly mark success/failure
    # when "tearDown" task with trigger rule is part of the DAG
    list(dag.tasks) >> watcher()

from tests.system.utils import get_test_run  # noqa: E402

# Needed to run the example DAG with pytest (see: tests/system/README.md#run_via_pytest)
test_run = get_test_run(dag)
